{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:33.840440Z",
     "start_time": "2025-12-23T08:39:33.100346Z"
    }
   },
   "source": [
    "# Hier werden alle verwendeten Pythonmodule importiert\n",
    "import Datenbank\n",
    "import Import\n",
    "import importlib\n",
    "import Koordinatentransformationen\n",
    "import sqlite3\n",
    "import Funktionales_Modell\n",
    "import Berechnungen\n",
    "import Parameterschaetzung\n",
    "import Stochastisches_Modell\n",
    "from Stochastisches_Modell import StochastischesModell\n",
    "import Export\n",
    "import Netzqualität_Genauigkeit"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:34.656550Z",
     "start_time": "2025-12-23T08:39:34.647503Z"
    }
   },
   "cell_type": "code",
   "source": [
    "importlib.reload(Datenbank)\n",
    "importlib.reload(Import)\n",
    "# Anlegen der Datenbank, wenn nicht vorhanden\n",
    "pfad_datenbank = r\"Campusnetz.db\"\n",
    "Datenbank.Datenbank_anlegen(pfad_datenbank)\n",
    "\n",
    "# Import vervollständigen\n",
    "imp = Import.Import(pfad_datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)"
   ],
   "id": "82d514cd426db78b",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:35.391343Z",
     "start_time": "2025-12-23T08:39:35.385408Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import der Koordinatendatei(en) vom Tachymeter\n",
    "pfad_datei = r\"Daten\\campsnetz_koordinaten_bereinigt.csv\"\n",
    "imp.import_koordinaten_lh_tachymeter(pfad_datei)"
   ],
   "id": "d3bce3991a8962dc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Der Import wurde abgebrochen, weil mindestens ein Teil der Punktnummern aus der Datei Daten\\campsnetz_koordinaten_bereinigt.csv bereits in der Datenbank vorhanden ist. Bitte in der Datei ändern und Import wiederholen.\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:35.987063Z",
     "start_time": "2025-12-23T08:39:35.973195Z"
    }
   },
   "cell_type": "code",
   "source": [
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "# Transformationen in ETRS89 / DREF91 Realisierung 2025\n",
    "print(db_zugriff.get_koordinaten(\"naeherung_lh\"))"
   ],
   "id": "196ff0c8f8b5aea1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'10009': Matrix([\n",
      "[1000.0],\n",
      "[2000.0],\n",
      "[ 100.0]]), '10006': Matrix([\n",
      "[   1000.0],\n",
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      "[  99.5825]]), '10010': Matrix([\n",
      "[1011.8143],\n",
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      "[  99.9259]]), '10018': Matrix([\n",
      "[1008.5759],\n",
      "[ 1942.762],\n",
      "[ 100.2553]]), '10008': Matrix([\n",
      "[979.7022],\n",
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      "[  99.732]]), '10005': Matrix([\n",
      "[ 966.5154],\n",
      "[2014.6496],\n",
      "[    99.72]]), '10003': Matrix([\n",
      "[ 908.4312],\n",
      "[1996.1248],\n",
      "[  99.7403]]), '10004': Matrix([\n",
      "[ 954.1536],\n",
      "[2021.6822],\n",
      "[  99.4916]]), '10007': Matrix([\n",
      "[ 921.7481],\n",
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      "[  99.9176]]), '10001': Matrix([\n",
      "[ 833.9439],\n",
      "[1978.3737],\n",
      "[  99.8946]]), '10002': Matrix([\n",
      "[ 875.9684],\n",
      "[1998.5174],\n",
      "[  99.5867]]), '10016': Matrix([\n",
      "[ 928.2783],\n",
      "[1944.0082],\n",
      "[ 100.0459]]), '10011': Matrix([\n",
      "[844.9567],\n",
      "[1891.157],\n",
      "[ 99.8117]]), '10026': Matrix([\n",
      "[1020.0059],\n",
      "[1913.8703],\n",
      "[ 100.3059]]), '10027': Matrix([\n",
      "[1016.9451],\n",
      "[1866.2914],\n",
      "[ 100.3251]]), '10043': Matrix([\n",
      "[1031.2077],\n",
      "[1822.4739],\n",
      "[ 100.3035]]), '10044': Matrix([\n",
      "[ 1025.976],\n",
      "[1782.4835],\n",
      "[ 100.5461]]), '10021': Matrix([\n",
      "[ 992.7607],\n",
      "[1904.8854],\n",
      "[ 100.3533]]), '10020': Matrix([\n",
      "[ 984.6187],\n",
      "[1903.3601],\n",
      "[ 100.3423]]), '10024': Matrix([\n",
      "[ 997.4831],\n",
      "[1881.7862],\n",
      "[ 100.3032]]), '10025': Matrix([\n",
      "[996.3241],\n",
      "[1866.844],\n",
      "[100.4102]]), '10022': Matrix([\n",
      "[990.0679],\n",
      "[1896.536],\n",
      "[100.2194]]), '10023': Matrix([\n",
      "[ 987.3223],\n",
      "[1889.8762],\n",
      "[  100.343]]), '10019': Matrix([\n",
      "[ 962.6387],\n",
      "[1902.3565],\n",
      "[  99.9772]]), '10033': Matrix([\n",
      "[ 964.0191],\n",
      "[1860.8023],\n",
      "[  99.8551]]), '10017': Matrix([\n",
      "[ 931.6761],\n",
      "[1900.9945],\n",
      "[  99.9572]]), '10052': Matrix([\n",
      "[ 1037.875],\n",
      "[1757.2999],\n",
      "[ 100.2737]]), '10042': Matrix([\n",
      "[1017.3489],\n",
      "[1803.0742],\n",
      "[ 100.3441]]), '10053': Matrix([\n",
      "[1033.3758],\n",
      "[1723.4258],\n",
      "[ 100.2774]]), '10037': Matrix([\n",
      "[ 966.2253],\n",
      "[1774.2051],\n",
      "[  99.9957]]), '10040': Matrix([\n",
      "[ 990.8832],\n",
      "[1780.9678],\n",
      "[ 100.1677]]), '10041': Matrix([\n",
      "[993.2769],\n",
      "[1812.031],\n",
      "[100.4749]]), '10038': Matrix([\n",
      "[ 958.1899],\n",
      "[1804.7135],\n",
      "[ 100.0741]]), '10051': Matrix([\n",
      "[1008.9811],\n",
      "[1750.1838],\n",
      "[  100.288]]), '10036': Matrix([\n",
      "[ 948.6403],\n",
      "[1763.5807],\n",
      "[ 100.0063]]), '10035': Matrix([\n",
      "[ 910.1265],\n",
      "[1768.0099],\n",
      "[ 100.0781]]), '10039': Matrix([\n",
      "[ 960.3884],\n",
      "[1820.0543],\n",
      "[ 100.0983]]), '10059': Matrix([\n",
      "[1049.2587],\n",
      "[1662.5451],\n",
      "[ 100.0148]]), '10050': Matrix([\n",
      "[1010.0246],\n",
      "[1726.2445],\n",
      "[ 100.1493]]), '10013': Matrix([\n",
      "[900.9076],\n",
      "[1902.873],\n",
      "[ 99.7911]]), '10028': Matrix([\n",
      "[ 853.9608],\n",
      "[1815.7417],\n",
      "[  99.7793]]), '10012': Matrix([\n",
      "[ 895.3032],\n",
      "[1924.1523],\n",
      "[  99.8758]]), '10014': Matrix([\n",
      "[ 913.9706],\n",
      "[1918.7731],\n",
      "[  99.8872]]), '10031': Matrix([\n",
      "[ 937.1557],\n",
      "[1855.2805],\n",
      "[  99.8479]]), '10015': Matrix([\n",
      "[ 912.5157],\n",
      "[1937.6471],\n",
      "[  99.9834]]), '10032': Matrix([\n",
      "[ 954.6732],\n",
      "[1845.9356],\n",
      "[   99.724]]), '10030': Matrix([\n",
      "[ 908.4749],\n",
      "[1828.8008],\n",
      "[  99.5581]]), '10029': Matrix([\n",
      "[ 909.3343],\n",
      "[1814.8767],\n",
      "[  99.5486]]), '10034': Matrix([\n",
      "[ 860.2357],\n",
      "[1758.9282],\n",
      "[   99.737]]), '10045': Matrix([\n",
      "[867.2324],\n",
      "[1705.063],\n",
      "[ 99.7214]]), '10049': Matrix([\n",
      "[ 985.2561],\n",
      "[1715.2109],\n",
      "[  99.9965]]), '10048': Matrix([\n",
      "[ 957.3889],\n",
      "[1716.2949],\n",
      "[  99.7212]]), '10047': Matrix([\n",
      "[ 929.5334],\n",
      "[1712.6429],\n",
      "[  99.6076]]), '10046': Matrix([\n",
      "[  910.663],\n",
      "[1716.0969],\n",
      "[  99.5459]]), '10057': Matrix([\n",
      "[969.6876],\n",
      "[1655.597],\n",
      "[ 99.7039]]), '10055': Matrix([\n",
      "[ 922.4731],\n",
      "[1647.7452],\n",
      "[  99.4658]]), '10054': Matrix([\n",
      "[ 860.4481],\n",
      "[1636.6722],\n",
      "[  99.7093]]), '10058': Matrix([\n",
      "[1013.2592],\n",
      "[1646.6356],\n",
      "[  99.8513]]), '10056': Matrix([\n",
      "[ 939.9763],\n",
      "[1636.4179],\n",
      "[  99.4027]])}\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:36.705490Z",
     "start_time": "2025-12-23T08:39:36.690491Z"
    }
   },
   "cell_type": "code",
   "source": [
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "# Transformationen in ETRS89 / DREF91 Realisierung 2025\n",
    "print(db_zugriff.get_koordinaten(\"naeherung_us\"))"
   ],
   "id": "3989b7b41874c16a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'10009': Matrix([\n",
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      "[ 546740.0869962516],\n",
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      "[ 546707.6385009313],\n",
      "[5080169.7334700795]]), '10010': Matrix([\n",
      "[3794758.6366199246],\n",
      "[ 546767.6665772106],\n",
      "[5080169.4644999765]]), '10018': Matrix([\n",
      "[3794762.2481267513],\n",
      "[ 546797.6912507551],\n",
      "[ 5080163.980380166]]), '10008': Matrix([\n",
      "[3794783.8581],\n",
      "[ 546746.6347],\n",
      "[5080152.7404]]), '10005': Matrix([\n",
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      "[546722.3209011297],\n",
      "[5080147.930942906]]), '10003': Matrix([\n",
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      "[5080111.543399332]]), '10004': Matrix([\n",
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      "[ 5080141.382390101]]), '10007': Matrix([\n",
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      "[5080116.663324944]]), '10001': Matrix([\n",
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      "[ 546729.5958],\n",
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      "[546788.7275390101],\n",
      "[5080116.868237535]]), '10011': Matrix([\n",
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      "[5080061.151341954]]), '10026': Matrix([\n",
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      "[ 546827.4296],\n",
      "[5080167.0938]]), '10027': Matrix([\n",
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      "[5080159.317534195]]), '10043': Matrix([\n",
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      "[ 546919.1497828952],\n",
      "[ 5080162.149716094]]), '10044': Matrix([\n",
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      "[  546834.470509102],\n",
      "[5080145.0361413695]]), '10024': Matrix([\n",
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      "[ 546857.095708699],\n",
      "[5080149.834714163]]), '10025': Matrix([\n",
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      "[ 546871.8107307912],\n",
      "[ 5080147.359175114]]), '10022': Matrix([\n",
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      "[ 546841.7501872958],\n",
      "[ 5080147.275074134]]), '10023': Matrix([\n",
      "[3794780.7952114563],\n",
      "[ 546848.1012091675],\n",
      "[ 5080144.924922213]]), '10019': Matrix([\n",
      "[3794800.0946706245],\n",
      "[ 546833.3239614451],\n",
      "[ 5080131.724532257]]), '10033': Matrix([\n",
      "[3794800.0160474544],\n",
      "[ 546874.6524563388],\n",
      "[ 5080127.204744104]]), '10017': Matrix([\n",
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      "[546831.6998861503],\n",
      "[5080113.374792286]]), '10052': Matrix([\n",
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      "[  546984.415934838],\n",
      "[ 5080157.831166813]]), '10042': Matrix([\n",
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      "[5080151.6103044115]]), '10053': Matrix([\n",
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      "[547017.5748381803],\n",
      "[5080150.930072506]]), '10037': Matrix([\n",
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      "[ 5080117.665]]), '10040': Matrix([\n",
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      "[546956.4249913145],\n",
      "[5080133.161471092]]), '10041': Matrix([\n",
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      "[5080138.722313838]]), '10038': Matrix([\n",
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      "[ 546929.7308726012],\n",
      "[  5080116.89880491]]), '10051': Matrix([\n",
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      "[ 546988.6993708528],\n",
      "[ 5080139.997874675]]), '10036': Matrix([\n",
      "[3794815.0546409036],\n",
      "[ 546969.5966706082],\n",
      "[ 5080106.064114862]]), '10035': Matrix([\n",
      "[3794845.948751911],\n",
      "[ 546961.512678588],\n",
      "[5080084.087510971]]), '10039': Matrix([\n",
      "[3794804.1623731344],\n",
      "[ 546914.7316360716],\n",
      "[ 5080120.139242563]]), '10059': Matrix([\n",
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      "[ 547079.4678],\n",
      "[5080152.3224]]), '10050': Matrix([\n",
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      "[ 547012.5266236273],\n",
      "[ 5080137.484970744]]), '10013': Matrix([\n",
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      "[ 546826.8685540904],\n",
      "[  5080095.43002485]]), '10028': Matrix([\n",
      "[3794889.7348],\n",
      "[ 546908.7636],\n",
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      "[ 546805.2364847381],\n",
      "[ 5080094.889461209]]), '10014': Matrix([\n",
      "[3794838.7464],\n",
      "[ 546812.3658],\n",
      "[   5080105.2]]), '10031': Matrix([\n",
      "[3794821.7594477106],\n",
      "[ 546877.5480584177],\n",
      "[ 5080110.746046175]]), '10015': Matrix([\n",
      "[3794839.4650256806],\n",
      "[ 546793.5165545414],\n",
      "[5080106.7712153485]]), '10032': Matrix([\n",
      "[3794807.848210704],\n",
      "[546888.4861254627],\n",
      "[5080119.745908576]]), '10030': Matrix([\n",
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      "[546901.0274418414],\n",
      "[5080090.356531718]]), '10029': Matrix([\n",
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      "[546914.9167077399],\n",
      "[5080089.099946169]]), '10034': Matrix([\n",
      "[3794886.104894752],\n",
      "[546965.6987415539],\n",
      "[ 5080053.40592357]]), '10045': Matrix([\n",
      "[3794881.900452307],\n",
      "[547019.7835874384],\n",
      "[5080050.715777841]]), '10049': Matrix([\n",
      "[3794786.8907962884],\n",
      "[ 547021.0765699627],\n",
      "[ 5080121.444681106]]), '10048': Matrix([\n",
      "[3794809.106679632],\n",
      "[547017.3023106218],\n",
      "[5080105.014391199]]), '10047': Matrix([\n",
      "[3794831.5349817923],\n",
      "[ 547018.2393882351],\n",
      "[ 5080088.124038595]]), '10046': Matrix([\n",
      "[3794846.5803718665],\n",
      "[ 547012.9971156706],\n",
      "[ 5080077.440420756]]), '10057': Matrix([\n",
      "[3794800.819370702],\n",
      "[ 547078.671611169],\n",
      "[ 5080104.57270624]]), '10055': Matrix([\n",
      "[3794838.851977278],\n",
      "[ 547081.903863645],\n",
      "[5080075.698247853]]), '10054': Matrix([\n",
      "[3794889.0494],\n",
      "[ 547086.9874],\n",
      "[5080038.1528]]), '10058': Matrix([\n",
      "[3794766.1088143717],\n",
      "[ 547091.7542871874],\n",
      "[ 5080129.120881729]]), '10056': Matrix([\n",
      "[3794825.041003442],\n",
      "[547094.8115741647],\n",
      "[5080084.488768324]]), '0645': Matrix([\n",
      "[3793994.4529],\n",
      "[ 495758.0093],\n",
      "[5085958.2047]]), '0648': Matrix([\n",
      "[3762551.5682],\n",
      "[ 538424.8576],\n",
      "[5104809.1503]]), '0656': Matrix([\n",
      "[3794838.5802],\n",
      "[ 546995.3112],\n",
      "[5080116.5503]]), '0995': Matrix([\n",
      "[3794519.9177],\n",
      "[ 588539.9138],\n",
      "[5075743.9332]]), '1675': Matrix([\n",
      "[3813621.0427],\n",
      "[ 566004.8947],\n",
      "[  5064056.93]]), 'ESTE': Matrix([\n",
      "[3816914.711],\n",
      "[ 507636.812],\n",
      "[5067733.467]]), 'GNA2': Matrix([\n",
      "[3767530.6335],\n",
      "[ 597990.0978],\n",
      "[5094563.5073]])}\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:39:37.518112Z",
     "start_time": "2025-12-23T08:39:37.508774Z"
    }
   },
   "cell_type": "code",
   "source": [
    "importlib.reload(Import)\n",
    "imp = Import.Import(pfad_datenbank)\n",
    "\n",
    "pfad_koordinaten_gnss = r\"Daten\\Koordinaten_OL_umliegend_bereinigt.csv\"\n",
    "# X, Y, Z der SAPOS-Stationen\n",
    "genauigkeit_sapos_referenzstationen = [0.05, 0.04, 0.09]\n",
    "\n",
    "imp.import_koordinaten_gnss(pfad_koordinaten_gnss, genauigkeit_sapos_referenzstationen)\n"
   ],
   "id": "7b6a359712fe858e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Import der Koordinaten aus stationärem GNSS abgeschlossen.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-23T08:50:23.112559Z",
     "start_time": "2025-12-23T08:50:23.042100Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Datumsgebende Koordinaten bestimmen\n",
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "\n",
    "liste_koordinaten_x = [10026]\n",
    "liste_koordinaten_y = [10059]\n",
    "liste_koordinaten_z = [10028]\n",
    "liste_koordinaten_x_y_z = [10008, 10001]\n",
    "\n",
    "db_zugriff.set_datumskoordinaten(liste_koordinaten_x, liste_koordinaten_y, liste_koordinaten_z, liste_koordinaten_x_y_z)\n",
    "\n",
    "# Datumgebende Koordinaten entfernen\n",
    "liste_koordinaten_x = [10026]\n",
    "liste_koordinaten_y = [10059]\n",
    "liste_koordinaten_z = [10028]\n",
    "liste_koordinaten_x_y_z = [10001]\n",
    "\n",
    "db_zugriff.set_datumskoordinaten_to_neupunkte(liste_koordinaten_x, liste_koordinaten_y, liste_koordinaten_z, liste_koordinaten_x_y_z)"
   ],
   "id": "5f786757ba89d5d0",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# ToDo: Sobald GNSS vorliegend Koordinaten im ETRS89 / DREF 91 (2025) daraus berechnen!\n",
    "#liste_koordinaten_naeherung_us = {\n",
    "#    10001: (3794874.984, 546741.752, 5080029.990),\n",
    "#    10002: (3794842.533, 546726.907, 5080071.133),\n",
    "#    10037: (3794774.148, 546955.423, 5080040.520),\n",
    "#    10044: (3794725.786, 546954.557, 5080084.411),\n",
    "#}\n",
    "\n",
    "\n",
    "#con = sqlite3.connect(pfad_datenbank)\n",
    "#cursor = con.cursor()\n",
    "#sql = \"\"\"\n",
    "#UPDATE Netzpunkte\n",
    "#SET naeherungx_us = ?, naeherungy_us = ?, naeherungz_us = ?\n",
    "#WHERE punktnummer = ?\n",
    "#\"\"\"\n",
    "#for punktnummer, (x, y, z) in #liste_koordinaten_naeherung_us.items():\n",
    "#    cursor.execute(sql, (x, y, z, punktnummer))\n",
    "#con.commit()\n",
    "#cursor.close()\n",
    "#con.close()"
   ],
   "id": "f64d9c01318b40f1",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# ToDo: Sobald GNSS-Daten vorliegen und die Berechnungen richtig sind, aufräumen!!!\n",
    "\n",
    "importlib.reload(Koordinatentransformationen)\n",
    "trafos = Koordinatentransformationen.Transformationen(pfad_datenbank)\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import itertools\n",
    "import numpy as np\n",
    "import sympy as sp\n",
    "\n",
    "db = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "dict_ausgangssystem = db.get_koordinaten(\"naeherung_lh\", \"Dict\")\n",
    "dict_zielsystem = db.get_koordinaten(\"naeherung_us\", \"Dict\")\n",
    "\n",
    "gemeinsame_punktnummern = sorted(set(dict_ausgangssystem.keys()) & set(dict_zielsystem.keys()))\n",
    "anzahl_gemeinsame_punkte = len(gemeinsame_punktnummern)\n",
    "\n",
    "liste_punkte_ausgangssystem = [dict_ausgangssystem[i] for i in gemeinsame_punktnummern]\n",
    "liste_punkte_zielsystem = [dict_zielsystem[i] for i in gemeinsame_punktnummern]\n",
    "\n",
    "def dist(a, b):\n",
    "    return float((a - b).norm())\n",
    "\n",
    "print(\"d(p2,p1)=\", dist(liste_punkte_ausgangssystem[1], liste_punkte_ausgangssystem[0]))\n",
    "print(\"d(P2,P1)=\", dist(liste_punkte_zielsystem[1], liste_punkte_zielsystem[0]))\n",
    "print(\"m0 ~\", dist(liste_punkte_zielsystem[1], liste_punkte_zielsystem[0]) /\n",
    "               dist(liste_punkte_ausgangssystem[1], liste_punkte_ausgangssystem[0]))\n",
    "\n",
    "\n",
    "def dist(a, b):\n",
    "    return float((a - b).norm())\n",
    "\n",
    "ratios = []\n",
    "pairs = list(itertools.combinations(range(len(liste_punkte_ausgangssystem)), 2))\n",
    "\n",
    "for i, j in pairs:\n",
    "    d_loc = dist(liste_punkte_ausgangssystem[i], liste_punkte_ausgangssystem[j])\n",
    "    d_ecef = dist(liste_punkte_zielsystem[i], liste_punkte_zielsystem[j])\n",
    "    if d_loc > 1e-6:\n",
    "        ratios.append(d_ecef / d_loc)\n",
    "\n",
    "print(\"Anzahl Ratios:\", len(ratios))\n",
    "print(\"min/mean/max:\", min(ratios), sum(ratios)/len(ratios), max(ratios))\n",
    "print(\"std:\", float(np.std(ratios)))\n",
    "\n",
    "S_loc = sum(liste_punkte_ausgangssystem, sp.Matrix([0,0,0])) / anzahl_gemeinsame_punkte\n",
    "S_ecef = sum(liste_punkte_zielsystem, sp.Matrix([0,0,0])) / anzahl_gemeinsame_punkte\n",
    "\n",
    "print(\"S_loc:\", S_loc)\n",
    "print(\"S_ecef:\", S_ecef)\n",
    "print(\"Delta:\", (S_ecef - S_loc).evalf(6))\n",
    "\n",
    "\n",
    "def dist(a, b):\n",
    "    return float((a - b).norm())\n",
    "\n",
    "n = len(liste_punkte_ausgangssystem)\n",
    "\n",
    "scores = []\n",
    "for i in range(n):\n",
    "    d_loc = []\n",
    "    d_ecef = []\n",
    "    for j in range(n):\n",
    "        if i == j:\n",
    "            continue\n",
    "        d_loc.append(dist(liste_punkte_ausgangssystem[i], liste_punkte_ausgangssystem[j]))\n",
    "        d_ecef.append(dist(liste_punkte_zielsystem[i], liste_punkte_zielsystem[j]))\n",
    "\n",
    "    d_loc = np.array(d_loc)\n",
    "    d_ecef = np.array(d_ecef)\n",
    "\n",
    "    # Verhältnisvektor; robust gegen Nullschutz\n",
    "    r = d_ecef / np.where(d_loc == 0, np.nan, d_loc)\n",
    "\n",
    "    # Streuung der Ratios für Punkt i\n",
    "    score = np.nanstd(r)\n",
    "    scores.append(score)\n",
    "\n",
    "for pn, sc in sorted(zip(gemeinsame_punktnummern, scores), key=lambda x: -x[1]):\n",
    "    print(pn, round(sc, 4))\n",
    "\n",
    "\n",
    "\n",
    "transformationsparameter = trafos.Helmerttransformation_Euler_Transformationsparameter_berechne()"
   ],
   "id": "21d60465e432c649",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Koordinatentransformationen)\n",
    "trafos = Koordinatentransformationen.Transformationen(pfad_datenbank)\n",
    "\n",
    "koordinaten_transformiert = trafos.Helmerttransformation(transformationsparameter)\n",
    "print(koordinaten_transformiert)"
   ],
   "id": "df0dcccb73299fcf",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "\n",
    "db_zugriff.set_koordinaten(koordinaten_transformiert, \"naeherung_us\")"
   ],
   "id": "f6993d81c8a145dd",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Importieren der tachymetrischen Beobachtungen\n",
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "\n",
    "db_zugriff.get_instrument_liste(\"Tachymeter\")\n",
    "db_zugriff.set_instrument(\"Tachymeter\", \"Trimble S9\")\n",
    "db_zugriff.get_instrument_liste(\"Tachymeter\")"
   ],
   "id": "e376b4534297016c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "#Importieren der apriori Genauigkeitsinformationen\n",
    "#Zulässige Beobachtungsarten = \"Tachymeter_Richtung\", \"Tachymeter_Strecke\"\n",
    "# Wenn Beobachtungsart = \"Tachymeter_Richtung\" --> Übergabe in Milligon und nur Stabw_apriori_konst\n",
    "# Wenn Beobachtungsart = \"Tachymeter_Strecke\" --> Übergabe Stabw_apriori_konst in Millimeter und Stabw_apriori_streckenprop in ppm\n",
    "\n",
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "importlib.reload(Berechnungen)\n",
    "\n",
    "db_zugriff.set_genauigkeiten(1, \"Tachymeter_Richtung\", 0.15)\n",
    "db_zugriff.set_genauigkeiten(1, \"Tachymeter_Strecke\", 0.8, 1)\n",
    "db_zugriff.set_genauigkeiten(1, \"Tachymeter_Zenitwinkel\", 0.15)"
   ],
   "id": "97e24245ce3398a2",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Importieren der tachymetrischen Beobachtungen\n",
    "importlib.reload(Import)\n",
    "imp = Import.Import(pfad_datenbank)\n",
    "\n",
    "pfad_datei_tachymeterbeobachtungen = r\"Daten\\campsnetz_beobachtungen_bereinigt.csv\"\n",
    "\n",
    "imp.import_beobachtungen_tachymeter(pfad_datei_tachymeterbeobachtungen, 1)"
   ],
   "id": "509e462917e98145",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Jacobimatrix aufstellen\n",
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "\n",
    "# Parameter des GRS80-ellipsoids (Bezugsellipsoid des ETRS89 / DREF 91 (2025)\n",
    "# ToDo: Quelle mit möglichst genauen Parametern heraussuchen!\n",
    "a = 6378137.0 #m\n",
    "b = 63567552.314 #m\n",
    "\n",
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "#db_zugriff.get_beobachtungen_id_standpunkt_zielpunkt(\"tachymeter_distanz\")\n",
    "Jacobimatrix_symbolisch = fm.jacobi_matrix_symbolisch()[0]\n",
    "Jacobimatrix_symbolisch_liste_unbekannte = fm.jacobi_matrix_symbolisch()[1]\n",
    "Jacobimatrix_symbolisch_liste_beobachtungsvektor = fm.jacobi_matrix_symbolisch()[2]"
   ],
   "id": "d38939f7108e1788",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Datenbank)\n",
    "db_zugriff = Datenbank.Datenbankzugriff(pfad_datenbank)\n",
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "A_matrix_numerisch_iteration0 = fm.jacobi_matrix_zahlen_iteration_0(Jacobimatrix_symbolisch, \"naeherung_us\", Jacobimatrix_symbolisch_liste_unbekannte, Jacobimatrix_symbolisch_liste_beobachtungsvektor)"
   ],
   "id": "4a0b1790c65d59ee",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "beobachtungsvektor_numerisch = fm.beobachtungsvektor_numerisch(Jacobimatrix_symbolisch_liste_beobachtungsvektor)"
   ],
   "id": "38f698b6694bebe7",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "beobachtungsvektor_naeherung_symbolisch = fm.beobachtungsvektor_naeherung_symbolisch(Jacobimatrix_symbolisch_liste_beobachtungsvektor)"
   ],
   "id": "e5cca13bbb6b95c5",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "beobachtungsvektor_naeherung_numerisch_iteration0 = fm.beobachtungsvektor_naeherung_numerisch_iteration0(Jacobimatrix_symbolisch_liste_beobachtungsvektor, beobachtungsvektor_naeherung_symbolisch)"
   ],
   "id": "eb0452c52e7afa6b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Auftstellen der Qll-Matrix\n",
    "importlib.reload(Stochastisches_Modell)\n",
    "stoch_modell = Stochastisches_Modell.StochastischesModell(A_matrix_numerisch_iteration0.rows)\n",
    "\n",
    "Qll_matrix_symbolisch = stoch_modell.Qll_symbolisch(pfad_datenbank, Jacobimatrix_symbolisch_liste_beobachtungsvektor)\n",
    "Qll_matrix_numerisch = stoch_modell.Qll_numerisch(pfad_datenbank, Qll_matrix_symbolisch,Jacobimatrix_symbolisch_liste_beobachtungsvektor)"
   ],
   "id": "40a3df8fe549c81",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "8e2aa544249c9d29",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "b479d3a946400ff6",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "",
   "id": "5d47e0771b22eb0b",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "\n",
    "importlib.reload(Parameterschaetzung)\n",
    "importlib.reload(Stochastisches_Modell)\n",
    "\n",
    "importlib.reload(Netzqualität_Genauigkeit)\n",
    "importlib.reload(Export)\n",
    "\n",
    "\n",
    "stoch_modell = Stochastisches_Modell.StochastischesModell(A_matrix_numerisch_iteration0.rows)\n",
    "\n",
    "dx = Parameterschaetzung.ausgleichung_global(A_matrix_numerisch_iteration0, fm.berechnung_dl(beobachtungsvektor_numerisch, beobachtungsvektor_naeherung_numerisch_iteration0), stoch_modell)[1]"
   ],
   "id": "f53849ee4757d5e8",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# Von Fabian\n",
    "\n",
    "importlib.reload(Funktionales_Modell)\n",
    "fm = Funktionales_Modell.FunktionalesModell(pfad_datenbank, a, b)\n",
    "importlib.reload(Export)\n",
    "importlib.reload(Datenbank)\n",
    "\n",
    "unbekanntenvektor_symbolisch = (fm.unbekanntenvektor_symbolisch(Jacobimatrix_symbolisch_liste_unbekannte))\n",
    "unbekanntenvektor_numerisch_iteration0 = fm.unbekanntenvektor_numerisch(Jacobimatrix_symbolisch_liste_unbekannte, unbekanntenvektor_symbolisch)\n",
    "print(unbekanntenvektor_numerisch_iteration0)\n",
    "print(\"-----\")\n",
    "unbekanntenvektor_numerisch = fm.unbekanntenvektor_numerisch(Jacobimatrix_symbolisch_liste_unbekannte, unbekanntenvektor_symbolisch, dx, unbekanntenvektor_numerisch_iteration0)\n",
    "print(unbekanntenvektor_numerisch)"
   ],
   "id": "122dca077d1d267c",
   "outputs": [],
   "execution_count": null
  }
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